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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Mitigating urban heat islands: Enhancing outdoor comfort with retro-reflective building materials through computational-fluid-dynamics analysis

Next Research

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Next Research 2025年第2期2卷

作者： Jihui Yuan Da Yan Xiong Xiao Yasuhiro Shimazaki Yupeng Wang Zhichao Jiao Zhengsong Lin Marko Bizjak Craig Farnham Kazuo Emura Department of Living Environment Design Grad. School of Human Life and Ecology Osaka Metropolitan University Osaka Japan Department of Architecture and Civil Eng. Grad. School of Eng. Toyohashi University of Technology Aichi Japan Building Energy Research Center School of Architecture Tsinghua University Beijing China School of Human Settlements and Civil Engineering Xi'an Jiaotong University No.28 Xianning West Road Xi'an Shaanxi Province China College of Art & Design Nanjing Forestry University Nanjing 210037 China Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 SI 2000 Maribor Slovenia

This study aims to delineate the origin, purpose, process, and results of the research, focusing on investigating the impact of different exterior wall materials on outdoor thermal indicators in Nagoya, Japan. The ultimate goal is to mitigate the Urban Heat Island (UHI) effect and improve urban outdoor thermal comfort. Computational Fluid Dynamics (CFD) is employed to evaluate two types of exterior wall materials: diffuse highly reflective (DHR) and retro-reflective (RR) materials. The research process involves assessing various outdoor thermal indices, such as wet-bulb globe temperature (WBGT), standard effective temperature (SET*), and universal thermal climate index (UTCI), in order to comprehend the thermal performance of each material. The results of the study indicate that the RR material consistently outperforms the DHR material in terms of outdoor thermal comfort, despite having slightly lower solar reflectance. This is supported by lower surface temperatures and surface heat flow associated with the RR material, suggesting reduced indoor cooling loads and significant energy savings for cooling purposes. These findings highlight the potential of RR material in mitigating the UHI effect and enhancing urban outdoor thermal conditions. By establishing a critical relationship between exterior wall materials and outdoor thermal indicators, this study proposes the adoption of RR materials for urban building exteriors as a sustainable and economically viable solution to improve urban outdoor thermal comfort and enhance the quality of life for residents.

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Online Internet Anomaly Detection With High Accuracy: A Fast Tensor Factorization Solution

Online Internet Anomaly Detection With High Accuracy: A Fast...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaocan Li Kun Xie Xin Wang Gaogang Xie Jigang Wen Guangxing Zhang Zheng Qin Department of Electrical and Computer Engineering State University of New York at Stony Brook USA College of Computer Science and Electronics Engineering Hunan University Changsha China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate traffic anomaly detection is very important, it very difficult to achieve. We propose to utilize tensor model to well exploit the multi-dimensional information hidden in the traffic data for more accurate online Internet anomaly detection. We decouple the tensor recovery problem to iteratively solve two sub problems, a tensor factorization sub-problem and an anomaly detection sub-problem. To reduce the high cost for computation and storage involved in tensor factorization, we propose two lightweight techniques to effectively derive factor matrices of tensor in the current window and iteration, taking advantage of tensor decomposition results of the previous window and iteration. We have done extensive experiments using two real traffic traces to compare with three tensor based algorithms and three matrix based algorithms. The experiment results demonstrate that our online anomaly detection algorithm can achieve the same anomaly detection accuracy as that of the best offline tensor based algorithm, but at 6100 times faster speed and with very low storage cost.

关键词： Anomaly detection Monitoring Matrix decomposition Microsoft Windows Principal component analysis Data models

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Efficiently Inferring Top-k Elephant Flows based on Discrete Tensor Completion

Efficiently Inferring Top-k Elephant Flows based on Discrete...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kun Xie Jiazheng Tian Xin Wang Gaogang Xie Jigang Wen Dafang Zhang College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York at Stony Brook USA State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Finding top- k elephant flows is a critical task in network measurement, with applications such as congestion control, anomaly detection, and traffic engineering. Traditional top- k flow detection problem focuses on using a small amount of memory to measure the total number of packets or bytes of each flow. Instead, we study a challenging problem of inferring the top- k elephant flows in a practical system with incomplete measurement data as a result of sub-sampling for scalability or data missing. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top- k elephant flows. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top- k flow inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top- k elephant flows with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. Extensive experiments using two real traces demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar data inference accuracy using significantly smaller time and storage space.

关键词： Inference algorithms Binary codes Data models Partitioning algorithms Indexes Computational modeling

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Next-Generation Sequencing Analysis of Extracellular Vesicle microRNAs for Breast Cancer Staging

Journal of Liquid Biopsy, The

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Journal of Liquid Biopsy, The 2023年 1卷 100106-100106页

作者： Martínez, C. González Sánchez, F. Ortega Garrido-Navas, C. Ortuño, F. Lorente, J. Serrano, M. Centro Pfizer-Universidad de Granada-Junta de Andalucía de Genómica e Investigación Oncológica (GENYO) Granada Spain Department of Legal Medicine Toxicology and Physical Anthropology Faculty of Medicine University of Granada Granada Spain Department of Genetics Faculty of Sciences University of Granada Granada Spain Department of Architecture and Computer Technology University of Granada Granada Spain Department of Medical Oncology Biosanitary Research Institute (IBS Granada) Virgen de las Nieves University Hospital Granada Spain

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Thermoelectric properties of layered ternary telluride Nb3SiTe6

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Physical Review Materials 2020年第9期4卷 094205-094205页

作者： Yahui Pang Emad Rezaei Dongyun Chen Si Li Yu Jian Qinsheng Wang Zhiwei Wang Junxi Duan Mona Zebarjadi Yugui Yao Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Micronano Centre Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing 100081 China Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia 22932 USA School of Physics and Electronics Hunan Normal University Changsha Hunan 410081 China Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22932 USA

We report the study of the thermoelectric properties of layered ternary telluride Nb3SiTe6. The temperature dependence of the thermoelectric power (TEP) evolves from nonlinear to linear when the thickness of the devices is reduced, consistent with the suppression of electron-phonon interaction caused by quantum confinement. The magnitude of TEP strongly depends on the hole density. It increases with decreasing hole density when the hole density is low, as observed in ionic-liquid-gated thin flakes. However, the device with the largest hole density possesses the highest TEP. Theoretical analysis suggests that the high TEP in the device with the largest hole density can be ascribed to the phonon-mediated intervalley scatterings. The highest TEP reaches ∼230μV/K at 370 K while the electrical resistivity of the device is maintained below 1.5mΩcm. Therefore, a large power factor PF ∼36μWcm−1K−2 comparable to the record values reported in p-type materials is obtained.

关键词： Seebeck effect Thermoelectric effects Topological materials Transport phenomena

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Corrigendum to “Optimization of 3D hydromagnetic Casson fluid flow over a permeable stretching surface using RSM and AI” [J. Radiat. Res. Appl. Sci. 18/2 101554]

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Journal of Radiation Research and Applied Sciences 2025年

作者： P. Lekkala V. Sannu P.K.D.N. Murthy V.V. Sibyala Charankumar Ganteda Muhammad Amer Qureshi Kamel Guedri Syed M. Hussain Abdulrazak H. Almaliki Mustafa Bayram Department of Mathematics Sai Rajeswari Institute of Technology Proddatur 516360 A.P India Department of Mathematics Vignan's Foundation for Science Technology and Research Vadlamudi Guntur 522213 A.P India Adithya College of Engineering and Technology Bengalore Karnataka India Department of Mathematics School of Applied Sciences REVA University Bengaluru Karnataka India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram 522302 Andhra Pradesh India Department of Mathematical and Physical Sciences College of Arts and Sciences University of Nizwa Nizwa Sultanate of Oman Mechanical Engineering Department College of Engineering and Architecture Umm Al-Qura University P. O. Box 5555 Makkah 21955 Saudi Arabia Department of Mathematics Faculty of Science Islamic University of Madinah Madinah 42351 Saudi Arabia Department of Civil Engineering College of Engineering Taif University Taif 21944 Saudi Arabia Department of Computer Engineering Biruni University 34010 Istanbul Turkey

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Research on visual tracking method for students’ browsing data in art literacy online education

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第4期5卷 218-225页

作者： Ye, Li Meng, Xiaojing Hang, Yongxin Tayeb, Shahab School of Architecture and Design China University of Mining and Technology No1 Daxue Road XuzhouJiangsu China School of Medical Information and Engineering Xuzhou Medical University No 209 Tongshan Road XuzhouJiangsu China China University of Mining and Technology No1 Daxue Road XuzhouJiangsu China Department of Electrical and Computer Engineering California State University Fresno United States

In order to improve the quality of student online education associated to the area of art literacy, it is necessary to track and identify their browsing data processes in real time. A visual tracking method for such activity, based on Hilbert’s spectrum anal-ysis, is proposed. A data mining model, a platform for online education that supports art literacy, has been constructed. The approach utilizes the spectral feature extraction method for data visualization pattern recognition, and Hilbert’s spectrum feature extraction algorithm. By using information fusion and feature matching, students in the same time period are scanned for spectral feature fusion and adaptive matching, and the fea-tures of Hilbert’s spectrum are extracted by segmentation. The extracted information is processed by visual spectral feature recognition, information recovery is realized, and visual tracking of browsing data for the cohort completed. The simulation results show that this method can effectively realize visual tracking and recognition of the students’ browsing data and improve the ability of data mining and information analysis. The research aims to show that the quality of online education of artistic literacy is improved through such practice. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Data mining

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Localized resolution of identity approach to the analytical gradients of random-phase approximation ground-state energy: Algorithm and benchmarks

arXiv

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arXiv 2021年

作者： Tahir, Muhammad N. Li, Jia Zhu, Tong Shang, Honghui Blum, Volker Ren, Xinguo Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Chemistry Duke University DurhamNC27708 United States State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China

We develop and implement a formalism which enables calculating the analytical gradients of particle-hole random-phase approximation (RPA) ground-state energy with respect to the atomic positions within the atomic orbital basis set framework. Our approach is based on a localized resolution of identity (LRI) approximation for evaluating the two-electron Coulomb integrals and their derivatives, and the density functional perturbation theory for computing the first-order derivatives of the Kohn-Sham (KS) orbitals and orbital energies. Our implementation allows one to relax molecular structures at the RPA level using both Gaussian-type orbitals (GTOs) and numerical atomic orbitals (NAOs). Benchmark calculations show that our approach delivers high numerical precision compared to previous implementations. A careful assessment of the quality of RPA geometries for small molecules reveals that post-KS RPA systematically overestimates the bond lengths. We furthermore optimized the geometries of the four low-lying water hexamers – cage, prism, cyclic and book isomers, and determined the energy hierarchy of these four isomers using RPA. The obtained RPA energy ordering is in good agreement with that yielded by the coupled cluster method with single, double and perturbative triple excitations, despite that the dissociation energies themselves are appreciably underestimated. The underestimation of the dissociation energies by RPA is well corrected by the renormalized single excitation correction. © 2021, CC BY.

关键词： Approximation algorithms

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2021年 197卷 vii-viii页

作者： Mealha, Óscar Rehm, Matthias Rebedea, Traian Department of Communication and Art Digital Media and Interaction Research Center University of Aveiro Aveiro Portugal Department of Computer Science University Politehnica of Bucharest Bucharest Romania Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark

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